Sensor Fusion
Showing 36 of 75 projects
An interactive Jupyter Notebook book teaching Kalman and Bayesian filters through Python code and practical examples.
An open-source simulator built on Unreal Engine for developing, training, and validating autonomous driving systems.
An open-source project for developing autonomous vehicle software with datasets, models, and ROS components.
A computationally efficient and robust LiDAR-inertial odometry (LIO) package using a tightly-coupled iterated Kalman filter.
A real-time, tightly-coupled lidar-inertial odometry package for robust robot localization and mapping.
An optimization-based multi-sensor state estimator for accurate self-localization in drones, cars, and AR/VR applications.
A multi-sensor calibration toolbox for autonomous driving, supporting IMU, LiDAR, camera, and radar calibration.
An open research-oriented C++ framework for multi-session and multi-robot visual-inertial mapping and localization.
An efficient probabilistic 3D mapping framework based on octrees for robotics and computer vision applications.
A ROS package for real-time 6DOF SLAM using 3D LIDAR, featuring graph-based optimization with multiple sensor constraints.
A ROS package providing nonlinear state estimation nodes for robot localization using sensor fusion.
A ROS package for robot-centric elevation mapping that handles pose uncertainty for navigation on rough terrain.
A ROS package for extrinsic calibration between LiDAR and camera sensors using 3D-3D point correspondences.
A target-less, automatic toolbox for LiDAR-camera extrinsic calibration that works with various sensor models without requiring calibration targets.
An open-source GNSS/INS simulation tool that generates sensor data, runs navigation algorithms, and visualizes results for inertial navigation systems.
A curated list of awesome LIDAR sensors, datasets, libraries, algorithms, and simulators for robotics and autonomous driving.
A curated list of awesome LIDAR sensors, datasets, libraries, algorithms, frameworks, and simulators for robotics and autonomous driving.
Fast and robust algorithm for segmenting Velodyne LiDAR point clouds into objects for autonomous driving applications.
A curated collection of papers, toolboxes, and notes for LiDAR-camera extrinsic calibration methods.
ROS tools for IMU devices including orientation filters and visualization plugins.
A ROS-based method for extrinsic calibration between a 3D LiDAR and a 6-DOF pose sensor using point cloud crispness optimization.
A tightly coupled 3D LiDAR-inertial odometry and mapping system for real-time robot localization and mapping.
A modular C++ and ROS 2 framework for building configurable LiDAR odometry and SLAM pipelines.
A deep learning-enhanced Kalman filter for accurate vehicle dead reckoning using only an IMU sensor.
A Python API for the Argoverse dataset, providing tools for 3D tracking, motion forecasting, and HD map interaction for autonomous vehicle research.
An open-source visual-inertial odometry system that estimates camera motion and sparse 3D maps from camera and IMU data.
A ROS framework for sensor fusion using nonlinear least squares optimization, enabling state estimation, localization, mapping, and calibration on robots.
A ROS voxel layer using OpenVDB for efficient 3D environment representation with temporal decay, replacing voxel_grid for navigation.
A C++ library for fast ground segmentation from LiDAR point clouds using the line-fit algorithm.
A lightweight, accurate, and robust monocular visual-inertial odometry system based on a hybrid Multi-State Constraint Kalman Filter.
Open-source software for precise vehicle localization using GNSS and IMU data fusion.
Real-time 3D semantic reconstruction library for robotics, building dense metric-semantic maps from 2D sensor data.
A benchmark dataset for long-range (up to 250m) dense depth estimation in autonomous driving, featuring 360° LiDAR ground truth.
A collection of code samples, unofficial FAQ, and index to supported modules for MicroPython development.
A collection of code samples, unofficial FAQ, and module index for MicroPython, covering hardware drivers, asyncio, GUIs, and embedded systems.
A robust system for multi-LiDAR extrinsic calibration, real-time odometry, and mapping without manual intervention.
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